Abstract

Query language modeling based on relevance feedback has been widely applied to improve the effectiveness of information retrieval. However, intra-query term dependencies (i.e., the dependencies between different query terms and term combinations) have not yet been sufficiently addressed in the existing approaches. This paper aims to investigate this issue within a comprehensive framework, namely the Aspect Query Language Model (AM). We propose to extend the AM with a Hidden Markov Model (HMM) structure, to incorporate the intra-query term dependencies and learn the structure of a novel Aspect Hidden Markov Model (AHMM) for query language modeling. In the proposed AHMM, the combinations of query terms are viewed as latent variables representing query aspects. They further form an Ergodic HMM, where the dependencies between latent variables (nodes) are modelled as the transitional probabilities. The segmented chunks from the feedback documents are considered as observables of the HMM. Then the AHMM structure is optimized by the HMM, which can estimate the prior of the latent variables and the probability distribution of the observed chunks. Our extensive experiments on three large scale TREC collections have shown that our method not only significantly outperforms a number of strong baselines in terms of both effectiveness and robustness, but also achieves better results than the AM and another state-of-the-art approach, namely the Latent Concept Expansion (LCE) model.